Transactions of Materials and Heat Treatment

Title：Prediction on cold rolling force based on improved ELM-AE

Authors： Zhang Zhiqiang Shang Meng Zhang Yu

Unit： Zhejiang Dongfang Polytechnic College of Anyang SMS Siemag Technology (Beijing) Co. Ltd.

KeyWords： cold rolling of strip steel rolling force neural network structure extreme learning machine autoencoder

ClassificationCode：TP183

year,vol(issue):pagenumber：2019,44(12):192-197

Abstract：

On the basis of studying the prediction of rolling force by deep neural network, a self-addition and deletion network structure optimization algorithm was proposed for the regression problem of extreme learning machine-autoencoder. Then, the features of the original data were extracted by the autoencoder to provide effective high-order features for the model. However, the learning speed of the extreme learning machine was fast and the generalization ability was strong, and the rolling force was regressed by the extreme learning machine in the supervision stage. Therefore, the network structure of the extreme learning machine was adjusted by adding and deleting the hidden layer nodes to solve the structural design issues of the extreme learning machine-autoencoder. Furthermore, the method was applied to rolling force regression, and the regression accuracy of the model was ensured by the deep network combining with a large amount of data. At the same time, the feature extraction of the rolling data and the self-addition and deletion of the network structure were realized. The result proves that the self-addition and deletion network of the deep structure has good model convergence and parameter regression ability, and it is superior to flexible RBF and sparse autoencoder neural network algorithm in training speed and accuracy.

Funds：

国家留学基金资助项目(201708410230)

张志强(1986-)，男，硕士，初级讲师 E-mail：zhf_2008@163.com

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